Science

Project Soli

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Project Soli is developing a new interaction sensor using radar technology. The sensor can track sub-millimeter motions at high speed and accuracy. It fits onto a chip, can be produced at scale and built into small devices and everyday objects.

 

5 INVENTIONS THAT WILL BLOW YOUR MIND

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https://www.youtube.com/watch?v=t0R0Xr0e-uk

Future of Healthcare by Microsoft

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This is an incredible video on how Microsoft sees the future in healthcare and how technology is improving our way of life! With state of the art hospitals being built in Malaysia; it’s just a matter of time before we experience seemless healthcare delivery. Malaysia Healthcare patients use a portable Personal Health Record (PHR) called the iPHER that carries all their PHI which includes, medications, lab tests, diagnosis, immunizations, alternative procedures, digital images, dental records, ophthalmic care (lens and contact prescriptions) and DNA any where in the world with no need to access the Internet to view the information. Malaysia Healthcare currently uses this PHR to reduce medical errors and create continuity of care for all their patients and to provide seemless healthcare delivery.

IBM Healthcare Industry: 2020 Vision

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As the global healthcare industry begins to redefine value and success for a more sustainable and value-based healthcare system, this video articulates the IBM vision for Smarter Healthcare, to engage the audience in a view for their future and IBM as their partner.

 

How do antidepressants actually work?

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A recent article by Deborah Orr regarding her experiences with antidepressants sparked a lot of debate as to their merits and drawbacks. The truth is, they’re not as simple or as understood as many might think

Doctors Warn That Anti-Depressants Can Lead To SuicideAnti-depressant pills named Fluoxetine are shown March 23, 2004 photographed in Miami, Florida. The Food and Drug Administration asked makers of popular antidepressants to add or strengthen suicide-related warnings on their labels as well as the possibility of worsening depression especially at the beginning of treatment or when the doses are increased or decreased. (Photo Illustration by Joe Raedle/Getty Images)
 Very common, but that doesn’t mean they’re perfectly understood. Photograph: Joe Raedle/Getty Images

Antidepressants; the go-to treatment for depression, or generalised anxiety. It’s incredible when you think about it, the fact that you can have a debilitating mood disorder, take a few pills, and feel better. It’s unbelievable that medical science has progressed so far that we now fully understand how the human brain produces moods and other emotions, so can manipulate them with designer drugs.

That’s right, it is unbelievable. Because it isn’t the case. The fact that antidepressants are now so common is something of a mixed blessing. On one hand, anything that helps reduce stigma and lets those afflicted know they aren’t alone can only be helpful. Depression is incredibly common, so this awareness can literally save many lives.

On the other hand, familiarity does not automatically mean understanding. Nearly everyone has a smartphone these days, but how many people, if pushed, could construct a touchscreen? Not many, I’d wager. And so it is with depression and antidepressants. For all the coverage and opinion pieces produced about them, the details around how they work remain somewhat murky and elusive.

Actually, in the case of antidepressants, it’s more a question of why they work, rather than how. Most antidepressants, from the earliest Trycyclics and Monamine Oxidase inhibitors, to the ubiquitous modern day selective serotonin reuptake inhibitors (SSRIs), work by increasing the levels of specific neurotransmitters in the brain, usually by preventing them from being broken down and reabsorbed into the neurons, meaning they linger in the synapses longer, causing more activity, so “compensating” for the reduced overall levels. Antidepressants make the remaining neurotransmitters work twice as hard, so overall activity is more “normal”, so to speak.

But knowing that antidepressants do this doesn’t actually explain how they end up alleviating depression. In a way, neurotransmitters are to the brain what the alphabet is to language; the basic elements of much richer, more complex contructs. So, boosting neurotransmitter levels throughout the brain doesn’t really tell us anything specific. It’s like having to restore a classic painting and being told it “needs more green”; that may be true, but where? How much? What shade? It’s too unspecific to tell us anything useful.

Depression is so poorly understood that most people illustrate it with someone holding their head in their hands, as a trawl through any image archive will reveal. It doesn’t make your brain heavier or anything.
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 Depression is so poorly understood that most people illustrate it with someone holding their head in their hands, as a trawl through any image archive will reveal. It doesn’t make your brain heavier or anything. Photograph: Nastia11/Getty Images/iStockphoto

The truth is, antidepressants were discovered largely by accident; Swiss scientists looking for treatments for schizophrenia in the 1950s realised a certain experimental substance caused euphoria in their subjects. And lo, antidepressants were born. Nothing unusual here, luck and serendipity are behind the discoveries of many drugs. But this led to the monoamine theory of depression, which argues that, because most antidepressants increase levels of neurotransmitters of the monoamine class, depression is caused by depletion of monoamines in the brain.

Except, the monoamine hypothesis is increasingly seen as insufficient. It’s part of what’s going on, sure, but not the whole story. For one, antidepressants boost neurotransmitter activity pretty much immediately, but therapeutic effects usually take weeks to kick in. Why? It’s like filling your car’s empty tank with petrol and it only starting to run again a month later; it means no fuel may have been a problem, but it’s clearly not the only problem.

There are other possible explanations. Neuroplasticity, the ability to form new connections between neurons, has been shown to be impaired in depressed patients. The theory is that this prevents the brain from responding “correctly” aversive stimuli and stress. Something bad happens, and the impaired plasticity means the brain is more ‘fixed’ as is, like a cake left out too long, preventing moving on, adapting, or escaping the negative mindset, and thus depression. Antidepressants also gradually increase neuroplasticity, so this may be actually why they work as they do, long after the transmitter levels are raised. It’s not like putting fuel in a car, it’s more like fertilising a plant; it takes time for the helpful elements to be absorbed into the system.

There are other possibilities. Inflammation causing undue stress on the brain is one, an overactive anterior cingulate cortex is another. Essentially, it’s complicated, and we can’t confirm anything yet.

Basically, depression isn’t a broken leg, or a cold. We can’t look at it, say “that’s what’s wrong, right there” and set about fixing it. Psychiatric issues are never that straightforward (and that’s without the many overlaps with more physical ailments). Part of the problem is that “depression” is more of an all-purpose term for something that manifests in many in different ways. It’s a mood disorder, but how mood is affected can vary substantially. Some end up with an unshakable black despair, others experience no mood to speak of, just feeling flat, empty and emotionless. Some (mostly men) become constantly angry and restless.

This is part of the reason why it’s proven so difficult to establish an underlying cause. The human brain is the most complex thing, and even a minor flaw or glitch in the workings can manifest in various, unexpected ways. And there’s no reason to assume that every case of depression has exactly the same cause. It’s not surprising, then, that antidepressants don’t work, or even make things worse, for many patients. There are other approaches too, but then these may not work for you either. If the causes and effects of depression vary considerably from person to person, so would the effectiveness of treatments.

Most therapeutic interventions don’t involve leather couches either. Maybe that’s a Hollywood thing?
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 Most therapeutic interventions don’t involve leather couches either. Maybe that’s a Hollywood thing? Photograph: Getty Images/Stockbyte

Antidepressants also have many potential side effects, which themselves vary from person to person. And while the therapeutic effects (which many argue are themselves overstated or based on questionable evidence) take weeks to occur, the same doesn’t apply to the unpleasant side effects, as Deborah Orr recently discovered.

Given all this, you may wonder how antidepressants ended up being so common in the first place? Well, it may boil down to the fact that, for all the flaws and problems they may have, they’re better than nothing, especially when the alternative is untreated depression. Some take a more cynical view, arguing that it’s pharmaceutical companies profiting by pushing profitable pills on people who don’t really need them.

Or, in the UK at least, it may be something to do lack of time and resources. In an ideal world, people with depression would have easy access to CBT or other interventions; given how every patient is different and what works for them is often a matter of trial and error. But, in an increasingly-underfunded and overworked NHS, this is increasingly difficult, even impossible, to offer. Many of the interpersonal therapies for depression and other disorders involve many hours of contact time with highly trained (ie expensive) professionals. Given the choice between that or giving someone a box of tablets and saying “see you in a month”, the latter would likely be the go-to option much of the time.

Overall, the widespread use of antidepressants is likely down to numerous complex causes, and the effects are unpredictable and confusing. Much like depression itself, which seems appropriate.

This article is adapted from Dean Burnett’s book The Idiot Brain, released in paperback in the US on 11 July.

Cancer detection: Seeking signals in blood

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Science  23 Feb 2018:
Vol. 359, Issue 6378, pp. 866-867
DOI: 10.1126/science.aas9102

Most cancers are detected when they cause symptoms that lead to medical evaluation. Unfortunately, in too many cases this results in diagnosis of cancers that are locally invasive or already metastatic and hence no longer curable with surgical resection or radiation treatment. Medical therapies, which might be curative in the setting of minimal tumor burden, typically provide more limited benefit in more advanced cancers, given the emergence of drug resistance (1). On page 926 of this issue, Cohen et al. (2) describe a strategy for early cancer detection, CancerSEEK, aimed at screening for multiple different cancers within the general population. This study challenges current assumptions in the field of blood-based biomarkers and sets the stage for the next generation of cancer screening initiatives.

Given the potential curative advantage of earlier diagnosis and treatment, why have so many cancer screening approaches failed? In the past, efforts at screening healthy populations for cancer have relied on tests that were insufficiently specific. For example, most men with rising serum prostate-specific antigen (PSA) do not have prostate cancer but instead have benign prostatic enlargement. However, where accurate tests exist, there have been dramatic improvements in cancer outcomes (3). For example, advanced cervical cancer has virtually disappeared in countries where Pap screening is the standard of care; although less reliable, mammography and screening colonoscopy are recommended for early detection of breast and colon cancers in individuals above ages 40 to 45 and 50, respectively, and screening heavy smokers by use of low-dose chest computed tomography (CT) scans reduces deaths from lung cancer (4). However, these tests are imperfect, and cost-effectiveness for broad deployment remains a challenge, particularly because a multitude of false-positive test results may lead to extensive diagnostic evaluations and unnecessary medical interventions. Unfortunately, for the majority of cancers no effective early screening tests are available.

It is in this setting that emerging molecular analyses of blood specimens, so-called “liquid biopsies,” are poised to revolutionize cancer screening (5). Circulating cell-free DNA (cfDNA) in the blood consists of small fragments of DNA that are approximately 150 nucleotides in length. cfDNA is primarily derived from normal tissues, but a small fraction may be derived from tumor cells in individuals who have cancer. This circulating tumor DNA (ctDNA) may be identified by the presence of characteristic mutations in cancer genes or by variations in chromosome copy numbers (6). Recent studies have established the reliability of ctDNA genotyping for monitoring treatment response and identifying drug resistance mechanisms in patients with advanced cancer (78). However, the much lower amount of ctDNA in the plasma of patients who have a localized tumor poses a challenge for early cancer screening, as does the absence of knowledge about which mutation to look for. Furthermore, some background mutations detectable in the blood may arise from nonmalignant proliferation of blood cells in older individuals, a phenomenon called clonal hematopoiesis of indeterminate potential (CHIP) (9). Importantly, cancer gene mutations alone are insufficient to identify the tissue of origin for a given cancer signal in the blood because similar mutations are present in multiple different cancers. Thus, a tissue-agnostic blood-based screening test has limited clinical utility, unless accompanied by insight into which organ should be investigated for follow-up.

How the CancerSEEK algorithm works

Plasma-based sequencing of 16 cancer genes generates an Omega score that is combined with eight cancer-associated serum proteins to derive a probability for having any of eight different types of cancer. A machine learning algorithm then integrates these data with 31 additional serum proteins and patient gender to predict the tissue of origin.

GRAPHIC: C. BICKEL/SCIENCE

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How the CancerSEEK algorithm works

Plasma-based sequencing of 16 cancer genes generates an Omega score that is combined with eight cancer-associated serum proteins to derive a probability for having any of eight different types of cancer. A machine learning algorithm then integrates these data with 31 additional serum proteins and patient gender to predict the tissue of origin.

GRAPHIC: C. BICKEL/SCIENCE

Cohen et al. sought to combine ctDNA sequencing of cancer genes with quantitation of tumor-associated serum protein markers, deriving a probabilistic algorithm for the presence of cancer and for the tissue of origin. After estimating the minimal number of recurrent cancer gene mutations required for a robust signal in eight different cancer types, Cohen et al. assigned an Omega score to condense the entirety of the ctDNA sequencing data into a single number, based on the most predictive mutation identified. Added to the Omega score are levels for eight cancer-associated serum proteins, which are combined by the CancerSEEK algorithm into a single probability of the sample having come from an individual with cancer (see the figure). Of the 1005 patients studied with operable cancers, the test sensitivity ranged from 33 to 98%, depending on the cancer type, with a test specificity in healthy blood donors greater than 99%. In patients correctly identified as having some type of cancer, a further algorithm that incorporates the Omega score and the initial eight protein panel, as well as measurements of an additional 31 proteins and the patient’s gender, correctly localizes the tumor to one of two top predicted anatomic sites in 83% of patients.

Among the key discoveries in this study is that a relatively small panel of cancer genes sequenced repeatedly to extreme depth to find rare alleles, with the pool of templates divided into multiple fractions in order to enhance signal detection, is sufficient to provide information for many different types of cancer. Compared with other efforts that use large-scale ctDNA sequencing (10), this approach will have greatly reduced cost. Also, by combining multiple protein biomarkers with ctDNA genotyping, the devised algorithm can implicate candidate tissues of origin, information unavailable from mutational data alone.

There are a number of important caveats. The predictive value of any diagnostic test relies on the prevalence of the disease within the tested population. For instance, in testing apparently healthy individuals within the general population, the prevalence of all eight cancers can be conservatively estimated as 1% of people over age 64 (11). Hence, in this setting even a test that is 99% sensitive and 99% specific will yield a positive predictive value (PPV) of only 50% (half of all test positives will be a false-positive result). Similarly, a positive CancerSEEK test result would be predicted to have a PPV of 40 to 45% for a person having any of the eight different cancers (2). Although the model was not designed to screen for individual cancer types, breaking down the aggregate PPV into its individual component cancers would result in further reduction in PPV, particularly for rare cancers. Because PPVs improve with higher disease prevalence, application of any cancer screening test to subpopulations with increased genetic or environmental risk factors (for example, carriers of familial breast cancer susceptibility mutations, heavy smokers at risk for lung cancer, or patients with liver cirrhosis predisposed to hepatocellular carcinoma) would of course increase the likelihood of true-positive results.

A well-documented challenge in early cancer detection studies is that patient populations at increased risk for cancer may also have precancerous or inflammatory conditions resulting in baseline elevation of serum protein biomarkers, a confounding factor that is not well recapitulated in the healthy control population used to build the CancerSEEK test. Although the relative contributions of ctDNA genotyping versus serum protein markers varies among the individual cancers analyzed by Cohen et al., the integration of these potentially orthogonal markers into the CancerSEEK algorithm is likely to strengthen its accuracy when trained on clinically relevant populations. Extending from this study, future research may combine multiple blood-based analytes, including massive ctDNA sequencing for mutations (10), high-throughput screening for chromosome copy number variation (12), scoring for tissue-specific DNA methylation patterns (13), serum-based multiprotein mass spectrometric quantitation (14), and digital quantitation of lineage-specific RNA from circulating tumor cells (15). Each of these blood-based assays may provide optimal capabilities for the detection of specific cancer types within at-risk patient populations, and as elegantly demonstrated by Cohen et al., combinations of tests may be optimal to enable both high-sensitivity detection and identification of the tissue of origin.

The ultimate goal of cancer screening is to diagnose invasive cancers early, while they are still curable. All the patients studied by Cohen et al. had been diagnosed as part of standard clinical evaluation and were candidates for surgical resection of their tumors, but many already had local invasion, and their cure rate is unknown. As the authors acknowledge, diagnosing cancers before clinical symptoms trigger an initial diagnostic procedure will require detection of even lower levels of signal, and prospective studies of patients whose cancer is first detected through blood-based screening will be required to determine real-world performance and whether such early screening can lead to improved cure rates. In addition, as the authors suggest, by coupling an initial blood-based screening test with secondary high-specificity confirmatory tests, it may be possible to achieve PPVs that would enable large-scale clinical implementation.

Undoubtedly, effective screening for early invasive cancers represents the best hope for reducing cancer mortality and morbidity. The conceptual advances and the practical feasibility of the CancerSEEK assay constitute an important milestone toward the application of early cancer detection. Most importantly, the ongoing development of cost-effective and accurate blood-based cancer screening strategies is poised to revolutionize clinical cancer care, bringing with it new emphasis on genetic and environmental risk stratification so as to tailor application of screening tests; minimally invasive imaging, biopsy, and molecular characterization of early tumors that are discovered and might be either indolent or invasive; and deployment of increasingly effective therapeutic options to stages of cancer for which they have curative potential. The vision of effective earlier cancer detection and intervention warrants validation in appropriate populations through large-scale clinical trials that are likely to radically change the way we diagnose and treat cancer.

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Holograms

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 High-efficiency color holograms created using a meta-surface made of nano-blocks
holograms
Color holographic image made by shining laser light on a metasurface. Credit: Wang et al. ©2016 American Chemical Society
(Phys.org)—By carefully arranging many nanoblocks to form pixels on a metasurface, researchers have demonstrated that they can manipulate incoming visible light in just the right way to create a color “meta-hologram.” The new method of creating holograms has an order of magnitude higher reconstruction efficiency than similar color meta-holograms, and has applications for various types of 3D color holographic displays and achromatic planar lenses.

The researchers, Bo Wang et al., from Peking University and the National Center for Nanoscience and Technology, both in China, have published a paper on the new type of hologram in a recent issue of Nano Letters.

The pixels on the new metasurface consist of three types of silicon nanoblocks whose precise dimensions correspond to the wavelengths of three different colors: red, green, and blue. To enhance the efficiency for the blue light, two identical nanoblocks corresponding to the blue light are arranged in each pixel, along with one nanoblock for red light and one for green light.

The researchers explain that each pixel can be thought of as a “meta-molecule” because it is the basic repeating, subwavelength unit of the larger metasurface that constitutes the entire hologram. The meta-molecules enable the metasurface to control light in ways that are not possible without modern nanoscale design.

When red, green, and blue lasers illuminate the hologram, each nanoblock manipulates the phase of its corresponding . The researchers explain that a key achievement of the study was to minimize the interactions between nanoblocks so that the nanoblocks function almost independently of each other. Then by orienting the nanoblocks in different ways, the researchers could change the light’s phase manipulation, resulting in different holographic images.

holograms
(Top left) One pixel, which is made of four nanoblocks. (Top right) Experimental setup for generating a color hologram. (Bottom) Experimental results of achromatic color holograms, made of red, green, or blue light or by combining these …more

“Our work provides an approach for realizing the almost independent manipulation of phase for different visible wavelengths in subwavelength resolution and in transmission mode due to the absence of interactions between nanoblocks within one meta-molecule, which allows for particular functionalities,” coauthor Yan Li, at Peking University, told Phys.org.

The researchers demonstrated that the nanoblock approach can be used to create two different types of holograms. In an achromatic hologram, the entire reconstructed image is in one color. By balancing the relative input of the three colors, a wide spectrum of colors can be achieved. In the second type of hologram, called a highly dispersive hologram, different parts of the reconstructed image have different colors—for example, a red flower, green stem, and blue container.

The new color  has a variety of potential applications where spectral wavefront manipulation is required, such as 3D color holograms, achromatic lenses, and anti-conterfeiting planar optical devices. The researchers plan to pursue these applications in future work.

“Based on this idea and approach, novel real planar optical devices may be fabricated to realize many novel or extra functions in the future,” Weiguo Chu at the National Center for Nanoscience and Technology said.

Read more at: https://phys.org/news/2016-07-high-efficiency-holograms-metasurface-nanoblocks.html#jCp